Process window optimization method, device, medium, program product and terminal

Abstract

The present application provides a process window optimization method, device, medium, program product and terminal, which solves the problems of low light intensity contrast in weak and bad point areas, limited process window and high defect rate of finished products through objective function optimization and numerical planning solution method. The sum of squares of logarithmic slopes of light intensity in weak and bad point areas is used as the optimization target, and a reasonable optimization model is established in combination with edge segment movement range, edge position error and mutual influence factors. At the same time, a variety of matrix methods such as sparse matrix and dense matrix are introduced to improve the optimization efficiency and accuracy, realize accurate optimization of mask graphics, expand the process window, reduce the defect rate, enhance process stability and adaptability, and significantly improve production efficiency and product quality.

Description

Technical Field

[0001] The present application relates to the field of semiconductor manufacturing, and in particular to a process window optimization method, device, medium, program product and terminal. Background Art

[0002] Photolithography is an indispensable core process in semiconductor manufacturing. With the continuous reduction of process nodes, the currently widely used 193 nanometer (nm) argon fluoride deep ultraviolet light source (ArF DUV) has become the mainstream choice for 28 nanometer and below processes. However, the critical feature size (Critical Dimension, CD) of the circuit has been significantly smaller than the wavelength of the light source, resulting in significantly enhanced optical interference and diffraction effects. In addition, the thickness effect of the mask and the characteristics of the photoresist material further affect the imaging quality, making the photolithography process face severe challenges.

[0003] To address these challenges, resolution enhancement techniques (RET) were introduced to improve imaging quality and expand the process window (PW). In RET, model-based optical proximity correction (MBOPC) is one of the important technologies. MBOPC uses physical optics simulation and optimization algorithms to correct areas in the mask design that may cause distortion, thereby improving the accuracy of the graphics and reducing the edge placement error (EPE). MBOPC forms an optimized mask graphic by splitting the boundaries of the design graphics into multiple edge segments and optimizing their positions, or optimizing the pixel distribution through an inverse lithography algorithm. The edge segment-based optimization method is more mainstream due to its adaptability and efficiency.

[0004] Although MBOPC performs well in overall optimization, it still has shortcomings in dealing with local process defects (such as weak spots and bad spots). Due to the complex optical interactions and fluctuations in process window parameters (such as focal length offset and exposure dose changes), weak and bad spot areas often lead to greater EPE fluctuations, becoming a bottleneck restricting the expansion of the process window. In addition, existing technologies mostly use a unified global objective function for optimization, which is difficult to effectively target local problems. At the same time, the complex optical interactions between edge segments are not considered enough, so the accuracy and efficiency of optimization are limited. At the same time, the lack of a clear regional division mechanism makes it difficult to accurately define the optimization range, which further reduces the overall optimization effect. Summary of the invention

[0005] In view of the shortcomings of the prior art mentioned above, the purpose of the present application is to provide a process window optimization method, device, medium, program product and terminal, which are used to solve the problems in the existing MBOPC technology, such as insufficient processing of weak and bad point areas, global limitations of optimization objectives and local correction capabilities, insufficient consideration of complex interactions between edge segments, and unclear intelligent zoning for regional optimization.

[0006] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides a process window optimization method, including: obtaining an original mask pattern; based on a preset logarithmic slope of light intensity, performing a defect point identification operation in the original mask pattern to extract multiple defect point coordinates; performing a region partitioning operation on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions; extracting multiple edge segments to be optimized in each of the optimizable sub-regions, and generating a mutual influence matrix based on the multiple edge segments to be optimized; based on the linear constraints and objective function corresponding to the current optimizable sub-region, performing an iterative optimization operation on the mutual influence matrix to generate a corrected edge segment; using the corrected edge segment to replace the edge segment at the corresponding position in the original mask pattern to generate an optimized mask pattern.

[0007] In some embodiments of the first aspect of the present application, the objective function includes: a weighted sum of the squares of the logarithmic slopes of the light intensities of all edge segments to be optimized in the current optimizable sub-region.

[0008] In some embodiments of the first aspect of the present application, the mutual influence matrix includes: a target point matrix, which represents the light intensity influence of each edge segment to be optimized on all target points when it moves, and the target points are extracted from each edge segment to be optimized by a preset method; and an auxiliary point matrix, which represents the light intensity influence of each edge segment to be optimized on all auxiliary points when it moves; the auxiliary points are obtained by moving the target points a unit distance along the normal direction of the edge segment to be optimized.

[0009] In some embodiments of the first aspect of the present application, the process of performing an iterative optimization operation on the interaction matrix according to preset linear constraints and objective functions includes: calculating the light intensity logarithmic slope matrix according to the target point matrix and the auxiliary point matrix; inputting the objective function and linear constraints into a linear programming solver or a quadratic programming solver to generate the edge segment movement amount of the current iteration round; performing movement on each edge segment to be optimized according to the edge segment movement amount of the current round to generate an updated edge segment; based on the updated edge segment, updating the light intensity logarithmic slope matrix, and determining whether the light intensity logarithmic slope matrix meets the preset convergence condition; if the preset convergence condition is not met, performing the iterative optimization operation again until the preset convergence condition is met.

[0010] In some embodiments of the first aspect of the present application, the process of calculating the light intensity logarithmic slope matrix based on the target point matrix and the auxiliary point matrix includes: calculating the difference between the target point matrix and the auxiliary point matrix to generate a difference matrix; dividing the difference matrix by the light intensity value corresponding to the edge segment to be optimized to generate the light intensity logarithmic slope matrix.

[0011] In some embodiments of the first aspect of the present application, the linear constraint conditions include one or more of the following: the movement range of each edge segment to be optimized; the allowable range of edge placement error of the original mask pattern edge segment, wherein the edge placement error is a linear function of the movement amount of the edge segment to be optimized.

[0012] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides a process window optimization device, which includes: a defect detection and partitioning module: used to obtain an original mask pattern; based on a preset logarithmic slope of light intensity, a defect point recognition operation is performed in the original mask pattern to extract multiple defect point coordinates; a region partitioning operation is performed on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions; an edge segment optimization module: used to extract multiple edge segments to be optimized in each of the optimizable sub-regions, and generate a mutual influence matrix based on the multiple edge segments to be optimized; based on the linear constraints and objective function corresponding to the current optimizable sub-region, an iterative optimization operation is performed on the mutual influence matrix to generate a corrected edge segment; a mask pattern update module: used to replace the edge segments at corresponding positions in the original mask pattern with the corrected edge segments to generate an optimized mask pattern.

[0013] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the process window optimization method when executed by a processor.

[0014] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides a computer program product, which includes a computer program code. When the computer program code runs on a computer, the computer implements the process window optimization method.

[0015] To achieve the above-mentioned purpose and other related purposes, the fifth aspect of the present application provides an electronic terminal, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program to implement the process window optimization method.

[0016] As described above, the process window optimization method, device, medium, program product and terminal of the present application have the following beneficial effects: the square sum of the logarithmic slope (ILS) of the light intensity in the weak and bad point areas is taken as the direct optimization target, thereby improving the optimization accuracy; the linearization range of the edge segment movement, the acceptable edge position error (EPE) and the mutual influence are comprehensively considered to ensure the feasibility of the optimization; by constructing a linear or quadratic programming problem, the optimal edge segment movement in the weak and bad point area is obtained, and the light intensity distribution is improved; a variety of matrix solution methods are used to improve the calculation efficiency and result accuracy; local optimization evaluation is implemented to ensure that the optimization results meet the actual process requirements; finally, the optimized design expands the lithography process window, reduces the finished product defect rate, and improves product quality and production efficiency. In summary, the present invention not only improves the lithography performance of the weak and bad point area, but also enhances the process adaptability, effectively reducing production costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of an embodiment of a process window optimization method of the present application is shown.

[0018] Figure 2 A structural schematic diagram of an embodiment of a process window optimization device of the present application is shown.

[0019] Figure 3 A structural schematic diagram of an embodiment of a process window optimization terminal of the present application is shown. DETAILED DESCRIPTION

[0020] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0021] Before further describing the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are applicable to the following interpretations:

[0022] <1> Intensity Log Slope: Intensity Log Slope (ILS) is the derivative of the logarithmic transformation of light intensity in spatial coordinates. It quantifies the gradient change of light intensity distribution and is often used to analyze the local optimization of light intensity in the lithography process and its impact on imaging quality.

[0023] <2> Interaction matrix: The interaction matrix is ​​used to represent the interaction between multiple decision variables. It is presented in the form of binary relationships, which makes it easier to consider the dependencies and influences between variables in the optimization model.

[0024] <3> Linear constraints: Linear constraints are constraints expressed in linear form in the optimization model, including linear equality and inequality constraints, which limit the possible values ​​of decision variables to meet the physical or technical requirements of practical applications.

[0025] <4> Objective function: The objective function is the core component of an optimization problem, expressed as a mathematical expression that aims to maximize or minimize a goal by adjusting the values ​​of decision variables, based on some performance metric or cost.

[0026] <5> Weighted sum: The weighted sum represents the sum of multiple input values ​​after weighted processing. It is often used in multi-objective optimization to integrate the relative importance of different objectives and form a single evaluation indicator.

[0027] <6> Target point: The target point is a specific solution or design goal of interest in the optimization process, defined as the optimization performance standard that you want to achieve, and is sought by the optimization algorithm within the feasible domain.

[0028] <7> Auxiliary points: Auxiliary points are additional decision variables or constraints introduced in the optimization process to guide the algorithm to search for a better solution and to improve the solvability of the model or the stability of the solution.

[0029] <8> Quadratic Programming Solver: Quadratic Programming Solver is used to solve optimization problems with quadratic objective functions and linear constraints, using specific algorithms (such as interior point method or gradient descent method) for efficient calculation to meet the needs of complex systems or industrial applications.

[0030] <9> Sparse matrix: A sparse matrix is ​​a special matrix structure in which most elements are zero. By utilizing this distinction, sparse matrices use optimized algorithms in storage and calculation to effectively reduce memory usage and speed up calculations. They are often used in large-scale optimization problems.

[0031] <10> Dense matrix: A dense matrix refers to a matrix in which most elements are non-zero. It uses a common storage format and calculation algorithm. It is suitable for application scenarios that need to process comprehensive data and reflects the integrity and complexity of matrix calculations.

[0032] <11> Eigenvalue decomposition: Eigenvalue decomposition is a technique for representing a square matrix as its eigenvalues ​​and eigenvectors. In this decomposition, the matrix is ​​converted into a diagonal matrix of its own eigenvalues ​​and a matrix of its eigenvectors. This method is important for studying the properties of matrices, dynamic systems, and data dimensionality reduction.

[0033] <12> LU decomposition: LU decomposition is a method of splitting a matrix into two simpler matrices, one of which is a lower triangular matrix and the other is an upper triangular matrix. This decomposition simplifies the process of solving linear equations, calculating the determinant of the matrix, and performing matrix inversion. LU decomposition requires the original matrix to be square and, in some cases, non-singular.

[0034] <13> QR decomposition: QR decomposition is a method to decompose a matrix into an orthogonal matrix and an upper triangular matrix. This decomposition is particularly effective for solving least squares problems and eigenvalue problems, and can improve the stability of numerical calculations. QR decomposition is widely used in computer science, signal processing, and other engineering applications.

[0035] <14> Conjugate gradient method: The conjugate gradient method is an iterative algorithm for solving large sparse linear equations caused by symmetric positive definite matrices. This method gradually approaches the optimal solution by gradually constructing conjugate directions and updating the solution vector. The conjugate gradient method has low memory requirements and computational complexity, and is particularly suitable for scientific computing and engineering fields that require efficient solution of large-scale mathematical models.

[0036] To facilitate understanding of the embodiments of the present application, first Figure 1 Detailed description. Figure 1 A schematic flow chart of a process window optimization method in an embodiment of the present invention is shown. The process window optimization method in this embodiment mainly includes the following steps:

[0037] Step S11: acquiring an original mask pattern; and performing a defect point recognition operation in the original mask pattern based on a preset light intensity logarithmic slope to extract a plurality of defect point coordinates.

[0038] In one embodiment of the present invention, the original mask pattern contains the basic shape and structure of the designed circuit features. The present invention innovatively proposes to identify defect points in the original mask pattern according to the preset logarithmic intensity slope (LIS) through existing software tools, so as to identify areas with poor imaging quality, including bad points and weak points, by analyzing the changes in light intensity in each area during the lithography process. Weak points refer to areas with relatively poor quality during the imaging process, which are manifested as blurred edges or insufficient contrast of the pattern. Weak point areas are affected by optical interference, mask material properties or photoresist properties, resulting in insufficient image clarity. The existence of weak points affects the process window (ProcessWindow, PW), that is, the fluctuation range of process parameters (such as focal length and exposure dose, etc.) allowed under the premise of ensuring imaging quality. If the number of weak points is too large, the process window may be reduced, thereby affecting the stability of production. Bad points refer to areas that do not meet the design requirements in imaging, which are often manifested as position offset, missing or other serious defects. Bad points are caused by uneven light sources, mask contamination or operation errors. Bad pixels affect image quality and have a negative impact on the performance and overall quality of the chip, further narrowing the process window.

[0039] Furthermore, the process of performing a defect point identification operation in the original mask pattern includes: the LIS feature of the weak point area shows a gentle change, the LIS value fluctuates within a small range, and the fluctuation amplitude gradually decreases. Exemplarily, when the LIS value remains at a relatively stable low level and fluctuates slightly, this area can be marked as a weak point, indicating that there is imaging blur or insufficient contrast. The LIS feature of the bad point area shows a sharp change, and the LIS value changes suddenly or jumps. When the LIS value changes significantly and quickly within a short distance (such as a sudden drop or rise to an extreme value), this area can be marked as a bad point, indicating the presence of serious imaging defects, such as position offset, missing or other graphic abnormalities.

[0040] Step S12: performing a region partitioning operation on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions.

[0041] In one embodiment of the present invention, the process of performing a region partitioning operation on the mask pattern where each defect point coordinate is located includes: dividing the mask pattern where each defect point coordinate is located into a core correction area, a peripheral disturbance area and an immutable mask bordering area, and formulating different optimization strategies according to the different characteristics of the three areas.

[0042] Specifically, the core correction area is a small area around the center line of the weak point or bad point, which can be significantly corrected on the basis of the original mask pattern. The reason is that the core correction area may contain adjacent transistor elements or other circuit characteristics, requiring its shape and size to be adjusted to improve the imaging quality. Exemplarily, if there is a point where the LIS feature fluctuates smoothly in the simulation results of the current mask, it is determined to be a weak point, and a circular area with a radius of 1-2 microns is defined as the core correction area with the weak point as the center. The peripheral disturbance area is located around the core correction area, and the purpose is to perform a perturbation correction on the original mask pattern to achieve an improvement in the imaging quality. The correction for the peripheral disturbance area is to perform a small floating correction on the basis of retaining the overall structure of the mask pattern. The unchangeable mask border area is the area farthest from the core correction area and the peripheral disturbance area, and the original mask pattern here is not corrected. This area is the circuit part that must remain unchanged during the lithography process to ensure the consistency and integrity of the overall structure. Exemplarily, an area containing fixed signal lines or grounding components is set as an unchangeable mask bordering area to ensure that the current component function must remain stable and reliable, and no form of lithography correction is allowed.

[0043] In this embodiment, the sub-regions included in the optimizable area include a core correction area and a peripheral disturbance area, and the sub-region included in the non-optimizable area is an immutable mask bordering area. Through the partition processing scheme proposed in the present invention, correction strategies can be flexibly formulated. For key components, such as transistors and interconnects, core correction and appropriate peripheral disturbances can improve the overall performance of the chip. For the immutable mask bordering area, its stability and integrity are the key to ensuring that the chip design can function normally. The present invention uses regional division to accurately control the correction amplitude of different areas through linear constraints and objective functions during the optimization process, thereby greatly improving the process window, significantly improving the imaging quality and overall performance of the chip, and achieving efficient semiconductor manufacturing.

[0044] It should be noted that the regional divisions and related parameters listed in this embodiment are for reference and explanation purposes only and are not restrictive. The method of the present invention can be used to autonomously adjust the settings and divisions of the optimizable regions and non-optimizable regions according to actual engineering requirements and process characteristics when applied, so as to more effectively meet specific application scenarios and optimization goals.

[0045] Step S13: extracting a plurality of edge segments to be optimized in each of the optimizable sub-regions, and generating a mutual influence matrix according to the plurality of edge segments to be optimized.

[0046] In one embodiment of the present invention, the mutual influence matrix includes: a target point matrix, which represents the influence of the light intensity of each edge segment to be optimized on all target points when moving, and the target points are extracted from each edge segment to be optimized by a preset method; and an auxiliary point matrix, which represents the influence of the light intensity of each edge segment to be optimized on all auxiliary points when moving; the auxiliary points are obtained by moving the target points a unit distance along the normal direction of the edge segment to be optimized.

[0047] In one embodiment of the present invention, the target point extraction method includes: on each edge segment to be optimized, the midpoint of the edge segment is selected as the target point. The reason is that the midpoint is the most representative point on the edge segment to be optimized, and can effectively reflect the light intensity impact of the edge segment on the adjacent area. When the edge segment is fine-tuned, the midpoint can be used to simplify the calculation, while ensuring that the imaging impact on the surrounding area can be accurately evaluated, which is suitable for the case where the edge segment is relatively uniform and has no complex structure.

[0048] Furthermore, the target point extraction method also includes: on each edge segment to be optimized, the first quarter, the second quarter and the midpoint of the edge segment are selected as target points. By selecting more than one target point, the effect of the edge segment shape change on the light intensity can be more comprehensively analyzed. Selecting unevenly distributed points such as the quarter and the third position can capture the light intensity changes of the edge segment at different positions, thereby improving the fine-grained evaluation of the lithography effect, which is suitable for edge segments with significant optical sensitivity.

[0049] Furthermore, the method of extracting the target point also includes: according to the distribution of other components around each edge segment to be optimized, selecting the point closest to a specific key component (such as a transistor, interconnection line, etc.) as the target point. This embodiment can ensure that the selected target point is closely related to important circuit characteristics, and can effectively evaluate the impact of the edge segment to be optimized on these key components. Selecting points close to these components can more accurately capture changes in light intensity and its potential impact on chip performance, which is particularly suitable for scenarios with dense interconnections or important functional blocks in circuit design. It should be noted that in this embodiment, multiple target points can be extracted in different ways.

[0050] In one embodiment of the present invention, the auxiliary point refers to a unit distance moved by the target point along the normal direction of the edge segment to be optimized, wherein the unit distance is determined by the processing accuracy, and the accuracy of the edge segment movement will directly affect the setting of the unit distance. Exemplarily, the unit distance can be set to 1 micron to ensure sufficiently accurate light intensity impact analysis during the lithography process.

[0051] In one embodiment of the present invention, the "light intensity impact" in the target point matrix and the auxiliary point matrix refers to the change in the light intensity distribution at the target point or the auxiliary point after the position or shape of the edge segment to be optimized is adjusted. Exemplarily, a slight adjustment of the edge segment will cause the light intensity of the target point to increase or decrease, and the light intensity impact is obtained by calculating the difference between the increase or decrease, thereby reflecting the degree of response of the edge segment change to the specific light intensity response. The target point matrix records the light intensity sensitivity of different target points to the edge segment change, while the auxiliary point matrix focuses more on analyzing the light intensity impact caused by such changes in the local area.

[0052] Step S14: Based on the linear constraints corresponding to the current optimizable sub-region and the objective function, an iterative optimization operation is performed on the interaction matrix to generate a modified edge segment.

[0053] In one embodiment of the present invention, according to preset linear constraints and objective functions, the process of performing iterative optimization operations on the interaction matrix includes: calculating the light intensity logarithmic slope matrix according to the target point matrix and the auxiliary point matrix; inputting the objective function and linear constraints into a linear programming solver or a quadratic programming solver to generate the edge segment movement amount of the current iteration round; performing movement on each edge segment to be optimized according to the edge segment movement amount of the current round to generate an updated edge segment; based on the updated edge segment, updating the light intensity logarithmic slope matrix, and determining whether the light intensity logarithmic slope matrix meets the preset convergence conditions. If the preset convergence conditions are not met, performing the iterative optimization operation again until the preset convergence conditions are met.

[0054] In this embodiment, the objective function includes but is not limited to: presetting parameters related to light intensity uniformity, minimizing the difference in light intensity distribution at a specific target point so that the light intensity value of the target point is as close as possible to a set reference value; and improving the process window by maximizing the weighted sum of the squared logarithmic slopes of all edge segments to be optimized in the current optimizable sub-area.

[0055] In this embodiment, the input of the linear programming solver includes a linear objective function and a set of linear constraints, and outputs the decision variable value that makes the objective function reach the minimum or maximum value; the input of the quadratic programming solver includes a quadratic objective function and linear constraints. The linear programming solver searches for the optimal solution by traversing the vertices of the feasible solution along the boundary of the constraint, or uses the interior point method to find the optimal solution in the feasible region to generate the optimal decision variable value. The quadratic programming solver includes gradually iterating and approximating the optimal solution within the feasible region by applying the interior point method, or converting a complex nonlinear problem into a series of quadratic programming sub-problems through a sequential quadratic programming method for iterative solution to generate the optimal solution.

[0056] In one embodiment of the present invention, the process of calculating the light intensity logarithmic slope matrix based on the target point matrix and the auxiliary point matrix includes: calculating the difference between the target point matrix and the auxiliary point matrix to generate a difference matrix; dividing the difference matrix by the light intensity value corresponding to the edge segment to be optimized to generate the light intensity logarithmic slope matrix.

[0057] In this embodiment, the intensity logarithmic slope matrix is ​​used to quantify the sensitivity and direction of the edge segment adjustment to be optimized to the intensity change of the target point and the auxiliary point. The objective function and the linear constraint conditions are used to perform quadratic programming to update the intensity logarithmic slope matrix to achieve layout optimization and process window expansion.

[0058] In one embodiment of the present invention, the objective function includes: a weighted sum of the squares of the logarithmic slopes of the light intensities of all edge segments to be optimized in the current optimizable sub-region.

[0059] In this embodiment, the square of the logarithmic slope of the light intensity of each edge segment to be optimized is multiplied by a weight coefficient. The weight can be adjusted based on the importance of different edge segments or their role in the imaging quality, so that the optimization target is more targeted. In this way, the impact of certain specific areas or edge segments on the final result can be given higher attention, thereby guiding the algorithm to optimize in a more appropriate direction. In addition, the reason for squaring the logarithmic slope of the light intensity is to emphasize significant differences. The squared value can enhance the sensitivity to large light intensity change rates, ensuring that the optimization algorithm pays more attention to those edge segments that have a significant impact on the quality of lithography imaging. This approach avoids the difficulty in reflecting the negative impact of light intensity changes, thereby improving the overall optimization effect.

[0060] In one embodiment of the present invention, the linear constraint conditions include one or more of the following: the movement range of each edge segment to be optimized; the allowable range of edge placement error of the original mask pattern edge segment, wherein the edge placement error is a linear function of the movement amount of the edge segment to be optimized.

[0061] In this embodiment, the moving range of the edge segment to be optimized refers to the maximum distance and the minimum distance that each edge segment can move during the optimization process. The present invention itself does not limit the moving mode of the edge segment to be optimized, and its moving mode includes but is not limited to: translation, rotation, and moving along the normal vector of the edge segment to be optimized. Limiting the moving range of the edge segment is to avoid mutual interference or overlap between the edge segments, thereby ensuring the independence of each edge segment and ensuring the stability and reliability of the optimization result.

[0062] In this embodiment, the linear function in the linear function of the original mask pattern and the linear function of the corrected edge segment movement distance is shown in Formula 1, where A is a preset linear transformation matrix, and b is the offset of the corrected edge segment relative to the corresponding edge segment in the original mask pattern.

[0063] Corrected edge position = A*original mask position + b (Formula 1)

[0064] The linear constraint in this embodiment also includes: edge placement error, which refers to the acceptable deviation range of the edge of the original mask pattern due to factors such as equipment accuracy and operation error during the manufacturing process. This means that the actual placed mask edge may be different from the designed edge, but this difference should be kept within a preset range.

[0065] In one embodiment of the present invention, the process of inputting the objective function and the linear constraints into a linear programming solver or a quadratic programming solver also includes: solving the light intensity logarithmic slope matrix using a variety of matrix solving methods to generate the optimal movement of the movable edge segment. The matrix solving methods include but are not limited to: sparse matrix solving, dense matrix solving, eigenvalue decomposition, LU decomposition, QR decomposition and conjugate gradient method. These solving methods each have their own applicable scenarios. By selecting a suitable matrix solving strategy, the computational efficiency and accuracy of the optimization process can be improved, thereby better meeting the requirements of the lithography process.

[0066] Step S15: using the corrected edge segments to replace the edge segments at corresponding positions in the original mask pattern to generate an optimized mask pattern.

[0067] In one embodiment of the present invention, the process of replacing the edge segments at corresponding positions in the original mask pattern with the corrected edge segments includes: finding the positions corresponding to the corrected edge segments in the original mask pattern, and replacing the edge segments at these positions with the corrected versions. This replacement ensures that the new mask pattern can more accurately reflect the optimized light intensity distribution and geometry. Finally, the generated optimized mask pattern is used in the actual lithography process to improve the imaging quality and consistency. To ensure the effectiveness of the new pattern, a local evaluation will be performed after the replacement is completed to verify whether its performance meets expectations, and further adjustments will be made if necessary.

[0068] Furthermore, the local evaluation includes: in the process of optimizing the mask pattern, a specific inspection of the newly generated mask pattern is performed to ensure that it meets the design requirements and production standards. The evaluation is mainly focused on areas with obvious changes in light intensity or complex shapes, so as to find problems in time and avoid defects in production. The steps of local evaluation include selecting the area to be evaluated, collecting data through simulation and actual measurement, comparing with the design standard, analyzing possible causes of deviation, and recording the evaluation results.

[0069] It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0070] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0071] Figure 2 is a schematic block diagram of a process window optimization device 200 provided in an embodiment of the present application. Figure 2 As shown, the device includes a defect detection and partitioning module 201, an edge segment optimization module 202 and a mask pattern updating module 203.

[0072] Defect detection and partitioning module 201: used to obtain an original mask pattern; based on a preset light intensity logarithmic slope, perform a defect point recognition operation in the original mask pattern to extract multiple defect point coordinates; perform a region partitioning operation on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions;

[0073] The edge segment optimization module 202 is used to extract multiple edge segments to be optimized in each of the optimizable sub-regions, and generate a mutual influence matrix according to the multiple edge segments to be optimized; based on the linear constraint conditions and the objective function corresponding to the current optimizable sub-region, perform an iterative optimization operation on the mutual influence matrix to generate a modified edge segment;

[0074] The mask pattern updating module 203 is used to replace the edge segments at corresponding positions in the original mask pattern with the corrected edge segments to generate an optimized mask pattern.

[0075] It should be understood that the specific process of each module executing the above corresponding steps has been described in detail in the above method embodiment, and for the sake of brevity, it will not be repeated here.

[0076] It should also be understood that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present application may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0077] Figure 3 is a schematic block diagram of an electronic terminal provided in an embodiment of the present application. Figure 3 As shown, the electronic terminal includes: at least one processor 301, a memory 302, at least one network interface 303 and a user interface 305. The various components in the device are coupled together through a bus system 304. It can be understood that the bus system 304 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 304 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 3 In the specification, various buses are labeled as bus systems.

[0078] The user interface 305 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0079] It is understood that the memory 302 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable categories of memory.

[0080] The memory 302 in the embodiment of the present invention is used to store various categories of data to support the operation of the electronic terminal 300. Examples of these data include: any executable program for operating on the electronic terminal 300, such as an operating system 3021 and an application 3022; the operating system 3021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 3022 may include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The process window optimization method provided by the embodiment of the present invention may be included in the application 3022.

[0081] The method disclosed in the above embodiment of the present invention can be applied to the processor 301, or implemented by the processor 301. The processor 301 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 301 or the instruction in the form of software. The above processor 301 may be a general processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 301 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general processor 301 can be a microprocessor or any conventional processor, etc. In combination with the steps of the accessory optimization method provided in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0082] In an exemplary embodiment, the electronic terminal 300 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0083] According to the method provided in the embodiments of the present application, the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes the process window optimization method of any embodiment shown in the above embodiments.

[0084] According to the method provided in the embodiments of the present application, the present application also provides a computer-readable storage medium, which stores a program code. When the program code runs on a computer, the computer executes the process window optimization method of any one of the embodiments shown above.

[0085] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program and / or a computer running on a processor. By way of illustration, both applications and computing devices running on a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).

[0086] Those of ordinary skill in the art will appreciate that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0091] In the above embodiments, the functions of each functional unit can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. Available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., high-density digital video discs (DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0092] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0094] In summary, the present application provides a process window optimization method, device, medium, program product and terminal. The present invention provides a process window optimization method. Through objective function optimization and numerical planning solution method, the problems of low light intensity contrast, limited process window and high defect rate of finished products in the traditional photolithography process are solved, and the accurate optimization of mask graphics and significant improvement of the quality of photolithography products are achieved. Specific measures include taking the square sum of the logarithmic slope of the light intensity in the weak point and bad point areas as the optimization target, focusing on improving the key areas. At the same time, a more reasonable optimization model is established by comprehensively considering the moving range of the edge segment, the acceptable edge position error and the mutual influence of the edge segment. In addition, sparse matrix, dense matrix and other matrix processing methods are introduced to improve the calculation efficiency and result accuracy of the optimization process. These improvements not only reduce the defect rate of photolithography products, but also enhance the adaptability and stability of the photolithography process to changing conditions, and improve production efficiency and product quality. Therefore, the present application effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0095] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A process window optimization method, characterized in that: include: Get the original mask pattern; Based on a preset light intensity logarithmic slope, a defect point recognition operation is performed in the original mask pattern to extract a plurality of defect point coordinates; Performing a region partitioning operation on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions; Extracting a plurality of edge segments to be optimized in each of the optimizable sub-regions, and generating a mutual influence matrix according to the plurality of edge segments to be optimized; Based on the linear constraints corresponding to the current optimizable sub-region and the objective function, an iterative optimization operation is performed on the interaction matrix to generate a modified edge segment; The corrected edge segments are used to replace the edge segments at corresponding positions in the original mask pattern to generate an optimized mask pattern.

2. The process window optimization method according to claim 1, characterized in that: The objective function includes: a weighted sum of the squares of the logarithmic slopes of the light intensities of all edge segments to be optimized in the current optimizable sub-region.

3. The process window optimization method according to claim 1, characterized in that: The interaction matrix includes: A target point matrix, which represents the light intensity influence of each edge segment to be optimized on all target points when moving, wherein the target points are extracted from each edge segment to be optimized by a preset method; And, the auxiliary point matrix represents the influence of each edge segment to be optimized on the light intensity of all auxiliary points when it moves; the auxiliary point is obtained by moving the target point along the normal direction of the edge segment to be optimized by a unit distance.

4. The process window optimization method according to claim 3, characterized in that: include: According to the preset linear constraints and the objective function, the process of performing iterative optimization operation on the interaction matrix includes: Calculate the light intensity logarithmic slope matrix according to the target point matrix and the auxiliary point matrix; Inputting the objective function and the linear constraint condition into a linear programming solver or a quadratic programming solver to generate an edge segment movement amount of a current iteration round; Move each edge segment to be optimized according to the edge segment movement amount of the current round to generate an updated edge segment; Based on the updated edge segments, the light intensity logarithmic slope matrix is ​​updated, and it is determined whether the light intensity logarithmic slope matrix meets a preset convergence condition. If the preset convergence condition is not met, the iterative optimization operation is performed again until the preset convergence condition is met.

5. The process window optimization method according to claim 4, characterized in that: include: According to the target point matrix and the auxiliary point matrix, the process of calculating the light intensity logarithmic slope matrix includes: Calculate the difference between the target point matrix and the auxiliary point matrix to generate a difference matrix; The difference matrix is ​​divided by the light intensity value corresponding to the edge segment to be optimized to generate the light intensity logarithmic slope matrix.

6. The process window optimization method according to claim 1, characterized in that: The linear constraint conditions include one or more of the following: the movement range of each edge segment to be optimized; the allowable range of edge placement error of the edge segment of the original mask pattern, wherein the edge placement error is a linear function of the movement amount of the edge segment to be optimized.

7. A process window optimization device, characterized in that: The device comprises: Defect detection and partitioning module: used to obtain the original mask pattern; based on the preset light intensity logarithmic slope, perform a defect point recognition operation in the original mask pattern to extract multiple defect point coordinates; perform a region partitioning operation on the mask pattern where each defect point coordinate is located to generate an optimizable region and a non-optimizable region; wherein the optimizable region contains one or more optimizable sub-regions; The edge segment optimization module is used to extract multiple edge segments to be optimized in each of the optimizable sub-regions, and generate a mutual influence matrix based on the multiple edge segments to be optimized; based on the linear constraints and objective function corresponding to the current optimizable sub-region, perform an iterative optimization operation on the mutual influence matrix to generate a modified edge segment; Mask pattern updating module: used to replace the edge segments at corresponding positions in the original mask pattern with the corrected edge segments to generate an optimized mask pattern.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the process window optimization method according to any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes computer program codes, and when the computer program codes are executed on a computer, the computer is enabled to implement the process window optimization method according to any one of claims 1 to 6.

10. An electronic terminal comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the process window optimization method according to any one of claims 1 to 6.

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